用4D雷达增强恶劣天气下的多传感器协同感知能力
4D Radar Meets LiDAR and Camera: Cooperative Perception under Adverse Weather
- 引入多普勒引导的空间注意力机制，实现多智能体融合
- 在雨雾中表现显著优于传统方案，雷达可替代失效的激光雷达
- 适用于真实场景迁移，支持自动驾驶全天候运行
协同感知对自动驾驶至关重要，但在恶劣天气下摄像头和激光雷达性能下降。本文通过引入4D成像雷达作为抗恶劣天气的模态，并提出多普勒引导的空间注意力机制，提升多智能体融合效果。方法扩展了两种代表性骨干网络：雷达-相机流水线（雷达替代激光雷达）与激光雷达-雷达流水线（雷达补充激光雷达）。为评估性能，我们发布了两个雷达增强基准数据集：OPV2V-R 和 Adver-City-R，其中包含基于物理的激光雷达退化模拟。实验表明，在雾和雨中均实现显著鲁棒性提升，尤其当雷达替代失效激光雷达时改善明显。在MAN TruckScenes上的额外验证证明了其从仿真到真实场景的泛化能力。结果表明，4D成像雷达是全天气协同感知的关键模态。数据集与代码已公开：https://url.fzi.de/SlimComm。
原文摘要 · Abstract (English)
Cooperative perception is important for autonomous driving but remains fragile when cameras and LiDAR degrade in adverse weather. We address this challenge by integrating 4D imaging radar as a weather-robust modality into collaborative perception and introducing a Doppler-guided spatial attention mechanism for multi-agent fusion. Our approach extends two representative backbones: a radar-camera pipeline where radar substitutes LiDAR, and a LiDAR-radar pipeline where radar complements LiDAR. To support evaluation, we release radar-augmented benchmarks, OPV2V-R and Adver-City-R, with physics-based LiDAR degradation. Experiments show strong robustness gains in fog and rain, including substantial improvements when radar replaces degraded LiDAR. Additional validation on MAN TruckScenes demonstrates transfer beyond simulation. Overall, our results highlight 4D imaging radar as a robust modality for all-weather collaborative perception. Dataset and code are available at: https://url.fzi.de/SlimComm.
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